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Found 10 papers, 4 papers with code
Chain-of-Verification Reduces Hallucination in Large Language Models
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.
The HCI Aspects of Public Deployment of Research Chatbots: A User Study, Design Recommendations, and Open Challenges
Publicly deploying research chatbots is a nuanced topic involving necessary risk-benefit analyses.
Improving Open Language Models by Learning from Organic Interactions
We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety.
Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
We compare models trained on our new dataset to existing pairwise-trained dialogue models, as well as large language models with few-shot prompting.
Infusing Commonsense World Models with Graph Knowledge
While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dynamically changing world.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best.
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks.
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model BlenderBot 2 (Chen et al., 2021) on open-domain knowledge-grounded conversations for the same number of parameters, in terms of consistency, knowledge and per-turn engagingness.
Internet-Augmented Dialogue Generation
The largest store of continually updating knowledge on our planet can be accessed via internet search.
